Two artificial intelligence must-haves: An innovation culture and data literacy

In my work as a data scientist, I have a front seat view of the power of artificial intelligence (AI) in turning vast data into actionable insights—insights that transform how organizations operate and the outcomes they achieve. The potential and promise of AI, and in particular generative AI (GenAI), have captured the world’s attention, but the path to successful implementation can be daunting, as AI technologies and use cases rapidly evolve.

In helping both commercial and government clients understand where AI can take their organizations, I’ve learned that there are certain fundamentals required for success, regardless of the type of organization, its industry, or its ambitions. Two of these fundamentals are building an innovation culture and ensuring data literacy. Additionally, we believe that all AI strategies should be grounded in a responsible use of AI framework (read more about this in Diane Gutiw’s blog).

The 2023 CGI Voice of Our Clients research confirms the importance of both AI innovation and data to business and technology executives across industries. Of the 1,764 executives we interviewed, AI tops their innovation investment plans over the next 3 years, and, today, 57% are investigating AI or doing proofs of concept. Executives also cite data management, governance, and quality as top improvement initiatives for their data strategies over the next three years.

Attributes of an innovation culture

Succeeding with AI requires, first and foremost, a strong innovation culture. Because AI is a fast-changing and fast-growing technology, organizations need a culture that drives AI innovation in a way that is agile, flexible, and motivating.

Agile: In terms of agility, when a new AI opportunity arises, how quickly can your organization respond? What type of approvals are required? How much red tape is involved? Can you get something started immediately, or are there hoops to jump through first? As the race to exploit GenAI intensifies across industries, agility is critical to responding first and fast.

Flexible: Flexibility also is vital, and talent is at the heart of a flexible culture. In pursuing a GenAI opportunity, who is available to do the work? Do they have the right skills, or is upskilling required? How quickly can they be transitioned to a project? Building a flexible culture requires investing in people with a wide variety of skills who can be moved quickly from project to project as new demands and opportunities emerge.

Motivating: You also need an innovation culture that is inspiring. Are your people excited by AI technologies and their potential? Do they want to learn new skills and take on new projects? Are there incentives for their efforts?

The ROI of cultural change

What can you expect from creating an innovation culture with these attributes? In terms of AI experimentation and adoption, I believe market differentiation is one of the biggest benefits, along with optimizing operations.

With this kind of culture, you can use GenAI to launch new products and services—better and faster than your competition. Ultimately, you can stay at the forefront of innovation, leading the charge versus waiting in the wings.

Investing in people and partnerships will help you to build such a culture. The right talent with the right skills, along with trusted partners that can provide training and access to tools that drive AI innovation, are key success factors.

Case in point

We’re working with a large communications company that has made this kind of cultural investment. As a result, they were able to embrace AI early on and pursue it on an ongoing basis. With the advent of GenAI, the company asked us to help them spin up a research and development (R&D) team to explore new use cases to continue their market innovation.

With their strong innovation culture in place, we were able to help them rapidly establish, expand and upskill the new GenAI R&D team, develop a proof-of-concept for a Gen AI-powered SQL query generator within eight weeks, and then implement a fully baked solution within four months. What’s more, the solution generated an immediate return on investment.

Unlocking the art of the possible

Data literacy involves knowing what data is available in each business area and how it can support data-driven decisions. To succeed with AI, you need a fundamental understanding of what’s possible, of what data exists, and how to use that data.

Ensuring this understanding across the enterprise is a business issue, not a technology one, and it requires a leadership-driven and training-oriented approach. Without insights about how AI can make their jobs easier, people are less likely to adopt solutions and identify valuable use cases.

From the top down, leadership should communicate the need for the organization to become a data-driven enterprise and how AI empowers that. Then, from the bottom-up, AI champions can be recruited to educate colleagues on the value of AI based on their own success in using the technology.

A major obstacle is that most people dislike change and often push back on new concepts and processes. However, it’s important for management to help employees overcome this challenge, and, even with pushback, move forward with the fundamentals required for innovation.

Overcoming the AI and data literacy change curve

A trusted external partner can help organizations create effective change management, training, and communications (CMTC) strategies to overcome the change curve. Such strategies often begin with executive communications to build awareness and excitement, as well as sharing stories of people whose day-to-day work has improved through AI and data. Training is provided across the organization for business leaders, technology teams, subject matter experts, and end users alike on the concepts of AI and data and what use cases are benefiting their industry, customers/citizens, and individual roles.

Case in point

For an energy infrastructure company, for example, one challenge was getting field technicians who maintain thousands of gas compressors to fully benefit from AI-driven predictive models to minimize costly downtime. We worked with the company to provide training, identify change champions, create testimonials, and implement a communications plan to instill a comfort level where technicians could combine their experience and intuition with insightful data from the models. The company saw AI model adoption increase—driving efficiency and increasing revenue—and is working to identify use cases in other business areas.

It’s exciting for me to see clients investing in innovation cultures and data literacy as they pursue emerging technologies such as AI. The rewards are clear, and the opportunities endless. If you’d like to learn more about the insights I’ve shared or CGI’s AI and data work in general, let’s have a conversation. See my contact information below.